基于Hyperion高光谱遥感数据的城市绿地信息提取方法的研究
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摘要
快速获取城市绿化信息对开展城市绿地保护、规划与建设城市绿地,对提高人们的环保意识、完善城市地价体系、规范绿地的经营管理等,都具有重要的理论和实践意义。传统的地面调查和测量方法费时、费力,并且动态更新比较困难,遥感技术提供了一种快速、宏观、动态提取城市绿地信息的方法。对城市信息提取来说,一些基于遥感图像分类和植被指数的传统信息提取方法通常是不准确的。
论文首先对Hyperion数据进行预处理。通过掩膜处理,得到研究区范围内的Hyperion影像。通过波段选取,剔除了未定标的波段和水汽影响严重的20个波段以及SWIR与VNIR重复的波段。通过进行辐射定标,将Hyperion数据由DN值转化成辐射亮度值。通过坏线修复和条带去除得到更为准确的图像。
通过大气辐射校正获取Hyperion数据反应的地表真实物理模型参数。本文采用的是基于MODTRAN4的FLAASH(Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes)大气纠正模块,大气辐射校正之后得到高光谱遥感影像中复原地物的地表反射率。
应用线性混合光谱模型提取绿地信息。首先运用PPI (纯净像元指数)进行样本提纯,得到最纯净的像元。同时,利用N-D散度法进行样本重组,确定最终光谱端元。然后利用线性光谱模型分解混合像元的方法对研究区高光谱遥感数据提取绿地信息,并与ISODATA法、监督分类中的最大似然分类方法(MLC)和NDVI密度分割方法的提取城市绿地结果进行比较。
结果表明,对城市绿地绿地信息而言,混合像元分解的总体精度和Kappa系数仅低于最大似然分类法,高于其他绿地提取方法。而混合像元分解方法制图精度远远高于最大似然分类方法,同时,混合像元分解方法的RMSE为0.00237,显示了较高的信息提取精度。因此,应用混合像元分解方法提取城市绿地更具有优势。
未来的研究将引入地面成像光谱仪来提供更加精确的地物纯净端元信息,进一步的研究将结合多个时相的Hyperion数据来研究城市绿地和城市其他地物组分信息的动态变化情况。
To obtain the urban compositional information quickly is of great theoretical and practical significance , it can increase the public environment protection consciousness and perfect the urban land price system, furthermore, it is of great importance for the regulation of green space management. Remote sensing technology offers an alternative to traditional ground-based survey of these green spaces. Traditional methods for the mapping of green space from remote sensing data such as classification techniques and vegetation indices were found to be inaccurate.
In this study we apply a linear spectral unmixing approach to hyperspectral data to map urban green space. Spectral mixing analysis(SMA) is an model based on the linear mixing of two or more pure spectral endmembers , it allows for variability in composition and illumination within an image. SMA gives the opportunity to categorise the scene into various sub-areas and guilds the endmember selection for a better adjustment of the model to different surface types.
EO-1 Hyperion data was used for this study, the Hyperion gathers near-continuous data in 242 discrete narrow bands along the 400–2500 nm spectral range at a 30 m spatial resolution and in 16 bits, and allows the relative contributions of different materials to the spectrally heterogeneous radiance field to be determined and their abundance to be mapped. Data processing occurred in several steps. The first of these was to select the useful bands, of the original 242 Hyperion bands, 176 bands were unique and calibrated, other bands were therefore dropped.
Then, the original Digital Number (DN) of Hyperion from the 176 bands were converted to radiance , using the DN-to-radiance conversion factors that accompanied the Hyperion data. The next step of preprocessing was the atmospheric correction to convert the radiance to surface reflectance. We processed the data using the above water reflectance spectra with an atmospherically corrected image. Images were corrected using the ENVI FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction software package. The FLAASH module incorporates MODTRAN 4 radiation transfer code with all MODTRAN atmosphere and aerosol types to calculate a unique solution for each image.
Finally, we specifically used a linear SMA (LSMA) model and performed a minimum noise fraction (MNF) transformation and pixel purity index (PPI) on EO-1 Hyperion image to derive the proportion of ground cover (green space,buildsings ) and water within a pixel.With the endmember spectra, a fully constrained linear mixture analysis was performed to generate fraction images for each endmember. The resulting green space fraction map showed the distribution and relative abundance of its endmember in the field.
The overall root mean square (RMS) errors was 0.237%, unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. The result indicates that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping green space in the urban area. Compared with other information extraction methods, we found that LSMM got better green space information results than ISODATA method、Maximum Likelihood Classification (MLC) method and NDVI density slice method, thus indicate that LSMM is a better information extraction method for extracting green space in the research area.
Future work will incorporate the imaging spectrometry to provide more accurate endmembers of the urban area as a reference, additionally, to detect the green space or other physical compositions, a time series of Hyperion data will be analyzed with the application of LSMM.
论文首先对Hyperion数据进行预处理。通过掩膜处理,得到研究区范围内的Hyperion影像。通过波段选取,剔除了未定标的波段和水汽影响严重的20个波段以及SWIR与VNIR重复的波段。通过进行辐射定标,将Hyperion数据由DN值转化成辐射亮度值。通过坏线修复和条带去除得到更为准确的图像。
通过大气辐射校正获取Hyperion数据反应的地表真实物理模型参数。本文采用的是基于MODTRAN4的FLAASH(Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes)大气纠正模块,大气辐射校正之后得到高光谱遥感影像中复原地物的地表反射率。
应用线性混合光谱模型提取绿地信息。首先运用PPI (纯净像元指数)进行样本提纯,得到最纯净的像元。同时,利用N-D散度法进行样本重组,确定最终光谱端元。然后利用线性光谱模型分解混合像元的方法对研究区高光谱遥感数据提取绿地信息,并与ISODATA法、监督分类中的最大似然分类方法(MLC)和NDVI密度分割方法的提取城市绿地结果进行比较。
结果表明,对城市绿地绿地信息而言,混合像元分解的总体精度和Kappa系数仅低于最大似然分类法,高于其他绿地提取方法。而混合像元分解方法制图精度远远高于最大似然分类方法,同时,混合像元分解方法的RMSE为0.00237,显示了较高的信息提取精度。因此,应用混合像元分解方法提取城市绿地更具有优势。
未来的研究将引入地面成像光谱仪来提供更加精确的地物纯净端元信息,进一步的研究将结合多个时相的Hyperion数据来研究城市绿地和城市其他地物组分信息的动态变化情况。
To obtain the urban compositional information quickly is of great theoretical and practical significance , it can increase the public environment protection consciousness and perfect the urban land price system, furthermore, it is of great importance for the regulation of green space management. Remote sensing technology offers an alternative to traditional ground-based survey of these green spaces. Traditional methods for the mapping of green space from remote sensing data such as classification techniques and vegetation indices were found to be inaccurate.
In this study we apply a linear spectral unmixing approach to hyperspectral data to map urban green space. Spectral mixing analysis(SMA) is an model based on the linear mixing of two or more pure spectral endmembers , it allows for variability in composition and illumination within an image. SMA gives the opportunity to categorise the scene into various sub-areas and guilds the endmember selection for a better adjustment of the model to different surface types.
EO-1 Hyperion data was used for this study, the Hyperion gathers near-continuous data in 242 discrete narrow bands along the 400–2500 nm spectral range at a 30 m spatial resolution and in 16 bits, and allows the relative contributions of different materials to the spectrally heterogeneous radiance field to be determined and their abundance to be mapped. Data processing occurred in several steps. The first of these was to select the useful bands, of the original 242 Hyperion bands, 176 bands were unique and calibrated, other bands were therefore dropped.
Then, the original Digital Number (DN) of Hyperion from the 176 bands were converted to radiance , using the DN-to-radiance conversion factors that accompanied the Hyperion data. The next step of preprocessing was the atmospheric correction to convert the radiance to surface reflectance. We processed the data using the above water reflectance spectra with an atmospherically corrected image. Images were corrected using the ENVI FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction software package. The FLAASH module incorporates MODTRAN 4 radiation transfer code with all MODTRAN atmosphere and aerosol types to calculate a unique solution for each image.
Finally, we specifically used a linear SMA (LSMA) model and performed a minimum noise fraction (MNF) transformation and pixel purity index (PPI) on EO-1 Hyperion image to derive the proportion of ground cover (green space,buildsings ) and water within a pixel.With the endmember spectra, a fully constrained linear mixture analysis was performed to generate fraction images for each endmember. The resulting green space fraction map showed the distribution and relative abundance of its endmember in the field.
The overall root mean square (RMS) errors was 0.237%, unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. The result indicates that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping green space in the urban area. Compared with other information extraction methods, we found that LSMM got better green space information results than ISODATA method、Maximum Likelihood Classification (MLC) method and NDVI density slice method, thus indicate that LSMM is a better information extraction method for extracting green space in the research area.
Future work will incorporate the imaging spectrometry to provide more accurate endmembers of the urban area as a reference, additionally, to detect the green space or other physical compositions, a time series of Hyperion data will be analyzed with the application of LSMM.
引文
[1]赵英时等.遥感应用分析原理与方法.北京:科学出版社,2003,194.
[2]马荣华,贾建华,胡孟春,等.基于RS和GIS方法的海南植被变化分析.北京林业大学学报.2001,23(1):7-10.
[3]张治英,徐德忠,周晓农,等.遥感图像非监督分类分析江宁县江滩钉螺孳生地植被特征.中华流行病学杂志.2003,24(4):261-265.
[4] Jan Knorn, Andreas Rabe, Volker C. Radeloff,etal.Land cover mapping of large areas using chain classification of neighboring Landsat satellite images.Remote Sensing of Environment,2009,113(5): 957-964.
[5] Lars Eklundh , Thomas Johansson , Svein Solberg.Mapping insect defoliation in Scots pine with MODIS time-series data.Remote Sensing of Environment,2009.
[6]李天平,刘洋,李开源.遥感图像优化迭代非监督分类方法在流域植被分类中的应用.城市勘测.2008,1:75-77.
[7]吴非权,马海州,沙占江.基于决策树与监督、非监督分类方法相结合模型的遥感应用研究.盐湖研究.2005,13(4):9-13.
[8]袁金国.森林植被遥感分类研究.河北师范大学学报(自然科学版).1999,23(2):274-277.
[9]刘良云,张兵,郑兰芬,等.利用温度和植被指数进行地物分类和土壤水分反演.红外与毫米波学报.2002,21(4):269-273.
[10]宫攀,陈仲新,唐华俊,等.基于MODIS温度/植被指数的东北地区土地覆盖分类.农业工程学报.2006,22(9):94-100.
[11]盛永伟,陈维英,肖乾广.利用气象卫星植被指数进行我国植被的宏观分类.科学通报.1995,40(1):68-71.
[12] Nicholas C. Coops , Michael A. Wulder , Donald Iwanicka.Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations.Remote Sensing of Environment,2009,113(6): 1250-1261.
[13] Tatsuro Nakaji, Reiko Ide, Kentaro Takagi,etal.Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Ja.Agricultural and Forest Meteorology,2008,148(5): 776-787.
[14] Pu,R., Gong, P.. Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data. Remote Sensing of Environment, 2008,112:939-954.
[15] Tromp,M., Epema, G.F..Spectral mixture analysis for mapping land degradation in semi-arid areas,”Geologie en Mijnbouw 1999,77:153-160 .
[16] Adams, J. B., Sabol, D.E..Classification of multispectral images based on fractions ofendmembers: Application to land cover change in the Brazilian Amazon.Remote Sensing of Environment,1995,52:137-154.
[17] Aguiar, A. P. D., Shimabukuro.Use of synthetic bands derived from mixture models in the multispectral classification of remote sensing images. International Journal of Remote Sensing, 1998,20:647-657.
[18] Cochrane, M. A.,Souza Jr., and C. M..Linear mixture model classificationof burned forests in the eastern Amazon.International Journal of RemoteSensing,1998,19:3433-3440.
[19] DeFries, R. S., Hansen, M. C., Townshend, J. R. G.Global continuous fieldsof vegetation characteristics: A linear mixture model applied to multiyear 8km AVHRR data. International Journal of Remote Sensing, 2000,21:1389-1414.
[20] Lu, D., Moran, E., and Batistella, M..Linear mixture model applied to Amazonian vegetation classification. Remote Sensing of Environment,2003, 87: 456-469.
[21] Lu, D., Batistella, M., Moran, E..Application of spectral mixture analysis to Amazonian land-use and land-cover classification.International Journal of Remote Sensing, 2004,25:5345-358.
[22] Phinn, S., Stanford, M..Monitoring the composition of urban environments based on the vegetation–impervious surface–soil (VIS) model by subpixel analysis techniques.International Journal of Remote Sensing,2002,23: 4131-4153.
[23] Rashed, T., Weeks, J. R., Gadalla, M. S.,“Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: A case study of the Greater Cairo region, Egypt.GeocartoInternational, 2001,16:5.
[24] Small, C..Estimation of urban vegetation abundance by spectral mixture analysis.International Journal of Remote Sensing, 2001,22:1305-1334.
[25] Small, C..Multitemporal analysis of urban reflectance.Remote Sensing of Environment,2002 ,81:427-442.
[26] Wu, C., Murray, A. T..Estimating impervious surface distribution by spectral mixture analysis.Remote Sensing of Environment, 2003,84:493-505.
[27]曲伟刚,高树新.航空遥感技术在威海市建成区建设规划中的应用.遥感信息,1995,2.
[28]徐介民.宁波市绿化航空遥感调查.勘察科学技术,1996,4:45-48.
[29]周廷刚,苏迎春.基于人工神经网络的城市航空遥感图像植被分类研究.西南师范大学学报(自然科学版),2004,29(6):1037-1040.
[30]高芳琴,吴健平,孙建中.基于航空遥感数据的绿地信息提取与制图系统.国土资源遥感,2001,2:57-61.
[31]叶勤,骆天庆.航空遥感在城市绿色三维量调查中的应用.铁路航测,2000,2.
[32]郝光荣,李胜,曲伟刚.航空遥感技术在城市绿化现状调查中的应用.测绘与空间地理信息,1997,2.39,48.
[33] Kevin Tansey, Ian Chambers, Andrew Anstee,etal.aerial photograph.Applied Geography,2009,29(2): 145-157
[34] L.T. Waser, E. Baltsavias, K. Ecker, H. Eisenbeiss.Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images.Remote Sensing of Environment,2008,112(5): 1956-1968.
[35]王利.应用TM图像解译滦河上游的植被分布.海河水利,1994,06.37-38.
[36]周文佐,潘剑君,房世波,等.应用TM影像分析南京城市生态绿地格局.城市环境与城市生态,2002,15(1):4-6.
[37]柳铮铮,曾从盛,钟春棋.基于TM影像的福州市地表植被变化分析.水土保持研究,2008,15(3):194-196.
[38]沈明霞,何瑞银,丛静华.基于ETM+遥感影像的森林植被信息提取方法研究.南京林业大学学报(自然科学版),2007,31(6):113-116.
[39]江洪,汪小钦,陈星.一种以FCD模型从SPOT影像提取植被覆盖率的方法.地球信息科学,2005,7(4):113-116.
[40]李玉凤,王波,李小明.基于SPOT5影像的山东南四湖地被覆盖分类研究.遥感技术与应用,2008,23(1):62-66.
[41]刘献伦,乔洪波,王广民,等.法国Spot5卫星遥感影像自动分类研究.山东林业科技,2008,4.18-19.
[42]于欢,张树清,崔利,等.基于CBERS-02遥感影像的湿地地表覆被分类研究.国土资源遥感,2008,4:69-74.
[43]何宇华,谢俊奇,刘顺喜.ALOS卫星遥感数据影像特征分析及应用精度评价.地理与地理信息科学,2008,24(2):23-26.
[44]易文斌,蒋卫国,国巧真,等.基于ALOS数据的城市景观格局信息提取研究—以北京市海淀区为例.遥感信息,2008,4:33-38.
[45]严海英.QUICKBIRD影像用于城市用地信息提取方法.测绘科学,2008,33(2):216-217.
[46]谭丽,何兴元,陈玮,等.基于QuickBird卫星影像的沈阳城市绿地景观格局梯度分析.生态学杂志,2008,27(7):1141-1148.
[47]初佳兰,张杰,王小龙,等.SPOT、QuickBird卫星遥感数据提取东沙岛植被信息的比较.海洋学研究,2006,24(2):79-85.
[48]张秀英,冯学智,丁晓东,等.基于面向对象方法的IKONOS影像城市植被信息提取.浙江大学学报(农业与生命科学版),2007,33(5):568-573.
[49] Le Wanga, Wayne P. Sousab, Peng Gong,etal.Comparison of IKONOS and QuickBird images for mapping mangrovespecies on the Caribbean coast of Panama.Remote Sensing of Environment, 2004,91:432–440.
[50]肖海燕,曾辉等.基于高光谱数据和专家决策法提取红树林群落类型信息.[J].遥感学报,2007,11(4):531-537.
[51]刘亮,姜小光等.利用高光谱数据进行农作物分类方法研究.[J].中国科学院研究生院学报,2006,23(4):484-488.
[52]陈志强,杨伟等.福州市植被信息提取及其景观格局空间变化.[J].福建地理,2006,21(2):95-99.
[53]刘玫岑,李霞等.ASTER数据在棉花信息提取中的应用-以兵团农一师为例[J].遥感技术与应用,2005,20(6):591-596.
[54] Wanxiao Sun , Shunlin Liang , Gang Xu,etal.Mapping plant functional types from MODIS data usingmultisource evidential reasoning.Remote Sensing of Environment,2008,112:1010–1024.
[55] Raymond F. Kokalya, Don G. Despainb, Roger N.Clark,etal.Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data.Remote Sensing of Environment ,2003,84: 437–456.
[56] Bruce W. Pengra a, Carol A. Johnston , Thomas R. Loveland.Mapping an invasive plant, Phragmites australis, in coastal wetlandsusing the EO-1 Hyperion hyperspectral sensor.Remote Sensing of Environment,2007,108:74–81.
[57]王芳,黎夏,卓莉等.基于Hyperion高光谱数据的城市植被胁迫评价[J].应用生态学报, 2007,18(6):1287-1293.
[58] Lenio Soares Galvao, Antonio Roberto Formaggio, Daniela Arnold Tisot.Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data.Remote Sensing of Environment, 2005, 94:523–534.
[59] Beck,R.,EO-1 User Guide,2003,8-9.
[60] Smith, M.O., S.L. Ustin, J.B. Adams . Gillespie 1990 Vegetation in deserts. I: a regional measure of abundance . Remote Sensing of Environment, 31: 1–26.
[61] Jensen,J.R.Introductory Digital Image Processing:A Remote Sensing Perspective,2 nd ed.Prentice-Hall,Englewood Cliffs,NJ,1996.
[62] Boardman.J.W.Automated spectral unmixing of AVIRIS data using convex geometry concepts:in summaries.Fourth JPL Airborne Geosciences Workshop,JPLPublication,1:11-14.
[63]李小娟,宫兆宁,刘小萌,等.ENVI遥感影像处理教程.北京,中国环境科学出版社,2007:388.
[64]彭望禄,余先川,周涛,等译.遥感与图像解译(第四版).北京,电子工业出版社,2003,403.
[65]刘湘南,佟志军,刘志明,等.遥感数字图像处理与分析.长春,吉林大学出版社,2005,220.
[66]赵英时等.遥感应用分析原理与方法.北京:科学出版社,2003,207.
[67]汤国安,张友顺,刘永梅,等.遥感数字图像处理.北京,科学出版社,2004,189.
[68]钱乐祥等.遥感数字影像处理与地理特征提取.北京,科学出版社,2004,171.
[69] Rouse.J.WMonitoring vegetation systems in the Great Plains with ERTS.Third Earth Resource Technology Satellite-1 Symposium,1973,1:309-317.
[2]马荣华,贾建华,胡孟春,等.基于RS和GIS方法的海南植被变化分析.北京林业大学学报.2001,23(1):7-10.
[3]张治英,徐德忠,周晓农,等.遥感图像非监督分类分析江宁县江滩钉螺孳生地植被特征.中华流行病学杂志.2003,24(4):261-265.
[4] Jan Knorn, Andreas Rabe, Volker C. Radeloff,etal.Land cover mapping of large areas using chain classification of neighboring Landsat satellite images.Remote Sensing of Environment,2009,113(5): 957-964.
[5] Lars Eklundh , Thomas Johansson , Svein Solberg.Mapping insect defoliation in Scots pine with MODIS time-series data.Remote Sensing of Environment,2009.
[6]李天平,刘洋,李开源.遥感图像优化迭代非监督分类方法在流域植被分类中的应用.城市勘测.2008,1:75-77.
[7]吴非权,马海州,沙占江.基于决策树与监督、非监督分类方法相结合模型的遥感应用研究.盐湖研究.2005,13(4):9-13.
[8]袁金国.森林植被遥感分类研究.河北师范大学学报(自然科学版).1999,23(2):274-277.
[9]刘良云,张兵,郑兰芬,等.利用温度和植被指数进行地物分类和土壤水分反演.红外与毫米波学报.2002,21(4):269-273.
[10]宫攀,陈仲新,唐华俊,等.基于MODIS温度/植被指数的东北地区土地覆盖分类.农业工程学报.2006,22(9):94-100.
[11]盛永伟,陈维英,肖乾广.利用气象卫星植被指数进行我国植被的宏观分类.科学通报.1995,40(1):68-71.
[12] Nicholas C. Coops , Michael A. Wulder , Donald Iwanicka.Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations.Remote Sensing of Environment,2009,113(6): 1250-1261.
[13] Tatsuro Nakaji, Reiko Ide, Kentaro Takagi,etal.Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Ja.Agricultural and Forest Meteorology,2008,148(5): 776-787.
[14] Pu,R., Gong, P.. Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data. Remote Sensing of Environment, 2008,112:939-954.
[15] Tromp,M., Epema, G.F..Spectral mixture analysis for mapping land degradation in semi-arid areas,”Geologie en Mijnbouw 1999,77:153-160 .
[16] Adams, J. B., Sabol, D.E..Classification of multispectral images based on fractions ofendmembers: Application to land cover change in the Brazilian Amazon.Remote Sensing of Environment,1995,52:137-154.
[17] Aguiar, A. P. D., Shimabukuro.Use of synthetic bands derived from mixture models in the multispectral classification of remote sensing images. International Journal of Remote Sensing, 1998,20:647-657.
[18] Cochrane, M. A.,Souza Jr., and C. M..Linear mixture model classificationof burned forests in the eastern Amazon.International Journal of RemoteSensing,1998,19:3433-3440.
[19] DeFries, R. S., Hansen, M. C., Townshend, J. R. G.Global continuous fieldsof vegetation characteristics: A linear mixture model applied to multiyear 8km AVHRR data. International Journal of Remote Sensing, 2000,21:1389-1414.
[20] Lu, D., Moran, E., and Batistella, M..Linear mixture model applied to Amazonian vegetation classification. Remote Sensing of Environment,2003, 87: 456-469.
[21] Lu, D., Batistella, M., Moran, E..Application of spectral mixture analysis to Amazonian land-use and land-cover classification.International Journal of Remote Sensing, 2004,25:5345-358.
[22] Phinn, S., Stanford, M..Monitoring the composition of urban environments based on the vegetation–impervious surface–soil (VIS) model by subpixel analysis techniques.International Journal of Remote Sensing,2002,23: 4131-4153.
[23] Rashed, T., Weeks, J. R., Gadalla, M. S.,“Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: A case study of the Greater Cairo region, Egypt.GeocartoInternational, 2001,16:5.
[24] Small, C..Estimation of urban vegetation abundance by spectral mixture analysis.International Journal of Remote Sensing, 2001,22:1305-1334.
[25] Small, C..Multitemporal analysis of urban reflectance.Remote Sensing of Environment,2002 ,81:427-442.
[26] Wu, C., Murray, A. T..Estimating impervious surface distribution by spectral mixture analysis.Remote Sensing of Environment, 2003,84:493-505.
[27]曲伟刚,高树新.航空遥感技术在威海市建成区建设规划中的应用.遥感信息,1995,2.
[28]徐介民.宁波市绿化航空遥感调查.勘察科学技术,1996,4:45-48.
[29]周廷刚,苏迎春.基于人工神经网络的城市航空遥感图像植被分类研究.西南师范大学学报(自然科学版),2004,29(6):1037-1040.
[30]高芳琴,吴健平,孙建中.基于航空遥感数据的绿地信息提取与制图系统.国土资源遥感,2001,2:57-61.
[31]叶勤,骆天庆.航空遥感在城市绿色三维量调查中的应用.铁路航测,2000,2.
[32]郝光荣,李胜,曲伟刚.航空遥感技术在城市绿化现状调查中的应用.测绘与空间地理信息,1997,2.39,48.
[33] Kevin Tansey, Ian Chambers, Andrew Anstee,etal.aerial photograph.Applied Geography,2009,29(2): 145-157
[34] L.T. Waser, E. Baltsavias, K. Ecker, H. Eisenbeiss.Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images.Remote Sensing of Environment,2008,112(5): 1956-1968.
[35]王利.应用TM图像解译滦河上游的植被分布.海河水利,1994,06.37-38.
[36]周文佐,潘剑君,房世波,等.应用TM影像分析南京城市生态绿地格局.城市环境与城市生态,2002,15(1):4-6.
[37]柳铮铮,曾从盛,钟春棋.基于TM影像的福州市地表植被变化分析.水土保持研究,2008,15(3):194-196.
[38]沈明霞,何瑞银,丛静华.基于ETM+遥感影像的森林植被信息提取方法研究.南京林业大学学报(自然科学版),2007,31(6):113-116.
[39]江洪,汪小钦,陈星.一种以FCD模型从SPOT影像提取植被覆盖率的方法.地球信息科学,2005,7(4):113-116.
[40]李玉凤,王波,李小明.基于SPOT5影像的山东南四湖地被覆盖分类研究.遥感技术与应用,2008,23(1):62-66.
[41]刘献伦,乔洪波,王广民,等.法国Spot5卫星遥感影像自动分类研究.山东林业科技,2008,4.18-19.
[42]于欢,张树清,崔利,等.基于CBERS-02遥感影像的湿地地表覆被分类研究.国土资源遥感,2008,4:69-74.
[43]何宇华,谢俊奇,刘顺喜.ALOS卫星遥感数据影像特征分析及应用精度评价.地理与地理信息科学,2008,24(2):23-26.
[44]易文斌,蒋卫国,国巧真,等.基于ALOS数据的城市景观格局信息提取研究—以北京市海淀区为例.遥感信息,2008,4:33-38.
[45]严海英.QUICKBIRD影像用于城市用地信息提取方法.测绘科学,2008,33(2):216-217.
[46]谭丽,何兴元,陈玮,等.基于QuickBird卫星影像的沈阳城市绿地景观格局梯度分析.生态学杂志,2008,27(7):1141-1148.
[47]初佳兰,张杰,王小龙,等.SPOT、QuickBird卫星遥感数据提取东沙岛植被信息的比较.海洋学研究,2006,24(2):79-85.
[48]张秀英,冯学智,丁晓东,等.基于面向对象方法的IKONOS影像城市植被信息提取.浙江大学学报(农业与生命科学版),2007,33(5):568-573.
[49] Le Wanga, Wayne P. Sousab, Peng Gong,etal.Comparison of IKONOS and QuickBird images for mapping mangrovespecies on the Caribbean coast of Panama.Remote Sensing of Environment, 2004,91:432–440.
[50]肖海燕,曾辉等.基于高光谱数据和专家决策法提取红树林群落类型信息.[J].遥感学报,2007,11(4):531-537.
[51]刘亮,姜小光等.利用高光谱数据进行农作物分类方法研究.[J].中国科学院研究生院学报,2006,23(4):484-488.
[52]陈志强,杨伟等.福州市植被信息提取及其景观格局空间变化.[J].福建地理,2006,21(2):95-99.
[53]刘玫岑,李霞等.ASTER数据在棉花信息提取中的应用-以兵团农一师为例[J].遥感技术与应用,2005,20(6):591-596.
[54] Wanxiao Sun , Shunlin Liang , Gang Xu,etal.Mapping plant functional types from MODIS data usingmultisource evidential reasoning.Remote Sensing of Environment,2008,112:1010–1024.
[55] Raymond F. Kokalya, Don G. Despainb, Roger N.Clark,etal.Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data.Remote Sensing of Environment ,2003,84: 437–456.
[56] Bruce W. Pengra a, Carol A. Johnston , Thomas R. Loveland.Mapping an invasive plant, Phragmites australis, in coastal wetlandsusing the EO-1 Hyperion hyperspectral sensor.Remote Sensing of Environment,2007,108:74–81.
[57]王芳,黎夏,卓莉等.基于Hyperion高光谱数据的城市植被胁迫评价[J].应用生态学报, 2007,18(6):1287-1293.
[58] Lenio Soares Galvao, Antonio Roberto Formaggio, Daniela Arnold Tisot.Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data.Remote Sensing of Environment, 2005, 94:523–534.
[59] Beck,R.,EO-1 User Guide,2003,8-9.
[60] Smith, M.O., S.L. Ustin, J.B. Adams . Gillespie 1990 Vegetation in deserts. I: a regional measure of abundance . Remote Sensing of Environment, 31: 1–26.
[61] Jensen,J.R.Introductory Digital Image Processing:A Remote Sensing Perspective,2 nd ed.Prentice-Hall,Englewood Cliffs,NJ,1996.
[62] Boardman.J.W.Automated spectral unmixing of AVIRIS data using convex geometry concepts:in summaries.Fourth JPL Airborne Geosciences Workshop,JPLPublication,1:11-14.
[63]李小娟,宫兆宁,刘小萌,等.ENVI遥感影像处理教程.北京,中国环境科学出版社,2007:388.
[64]彭望禄,余先川,周涛,等译.遥感与图像解译(第四版).北京,电子工业出版社,2003,403.
[65]刘湘南,佟志军,刘志明,等.遥感数字图像处理与分析.长春,吉林大学出版社,2005,220.
[66]赵英时等.遥感应用分析原理与方法.北京:科学出版社,2003,207.
[67]汤国安,张友顺,刘永梅,等.遥感数字图像处理.北京,科学出版社,2004,189.
[68]钱乐祥等.遥感数字影像处理与地理特征提取.北京,科学出版社,2004,171.
[69] Rouse.J.WMonitoring vegetation systems in the Great Plains with ERTS.Third Earth Resource Technology Satellite-1 Symposium,1973,1:309-317.